Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking
Authors: Isidoros Tziotis, Constantine Caramanis, Aryan Mokhtari
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Numerical Experiments In this section, we compare PDGT with a simple version of D-GET where each node has full knowledge of its local gradient. ... In Fig. 1 the experiment is run for 10 nodes, and the target rank is 20. ... In Fig. 2, the experiment is run for 30 nodes and the target rank is 30. |
| Researcher Affiliation | Academia | Isidoros Tziotis, Constantine Caramanis, Aryan Mokhtari Department of Electrical and Computer Engineering The University of Texas at Austin EMAIL |
| Pseudocode | Yes | Algorithm 1: PDGT algorithm, Algorithm 2: PDGT algorithm: Phase I, Algorithm 3: PDGT algorithm: Phase II |
| Open Source Code | No | The paper does not explicitly state that source code for the methodology is provided or available. |
| Open Datasets | Yes | We focus on a matrix factorization problem for the Movie Lens dataset, where the goal is to ο¬nd a rank r approximation of a matrix M Ml n, representing the ratings from 943 users to 1682 movies. |
| Dataset Splits | No | The paper does not explicitly specify training, validation, or test dataset splits (e.g., percentages, sample counts, or specific splitting methodology). |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models, or cloud resources) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The stepsize for D-GET and both phases of PDGT is 3. Regarding the parameters of PDGT we set the number of rounds during phase I and II to be 1500 and 100, respectively. Further, we set the threshold before we add noise during phase I as presented in (8) to be 10^-6 and the radius of the noise injected to be 4. |